Attention-Guided Efficientnet Architecture For Precise Criminal Identification in Surveillance Images
arXiv:2607.03073v1 Announce Type: cross Abstract: Criminal identification from surveillance imagery has become a critical research area in intelligent forensic surveillance systems due to the increasing deployment of CCTV cameras in public and private environments. However, surveillance-based face recognition remains highly challenging because of low image resolution, illumination variation, motion blur, pose changes, facial occlusion, and background clutter. To address these limitations, this paper proposes an Attention-Guided EfficientNet (AG-EfficientNet) framework for precise criminal iden
The proliferation of CCTV cameras and advancements in AI, particularly deep learning and attention mechanisms, are making sophisticated surveillance systems economically viable and technically achievable.
Precise criminal identification via AI in surveillance dramatically enhances state and corporate monitoring capabilities, impacting privacy, civil liberties, and law enforcement effectiveness globally.
The ability to accurately identify individuals in challenging surveillance conditions shifts the paradigm of public safety and personal anonymity, making ubiquitous AI-driven monitoring more feasible.
- · Surveillance technology companies
- · Law enforcement agencies
- · Smart city initiatives
- · AI developers
- · Privacy advocates
- · Civil liberties organizations
- · Individuals seeking anonymity
- · Convicted criminals
Wider adoption of AI-powered facial recognition systems in public and private sectors for security and identification.
Increased legislative debates and legal challenges regarding civil liberties, data privacy, and the ethical deployment of AI surveillance.
Potential for development of counter-surveillance technologies and tactics by individuals or groups seeking to evade detection.
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Read at arXiv cs.AI